Skip to content

Instantly share code, notes, and snippets.

Discover gists

@karpathy
karpathy / llm-wiki.md
Created April 4, 2026 16:25
llm-wiki

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@apollo-mg
apollo-mg / gist_sm60_fast_fp16.md
Last active July 14, 2026 12:08
**The P100 has been doing silently noisy math in llama.cpp for years. Three lines fix it for free.**

The P100 has been doing silently noisy math in llama.cpp for years. Three lines fix it for free.

TL;DR

llama.cpp's CUDA backend has a FAST_FP16_AVAILABLE flag: "this GPU is fast at fp16, so do quality-sensitive math in fp16." The GTX 10-series (sm_61) was exempted from it long ago. The Tesla P100 (sm_60) never was — because GP100 is the one Pascal chip with fast fp16 hardware. Hardware can, therefore software did. Nobody measured what it cost.

@YoraiLevi
YoraiLevi / ssh-symlink-redirectionguard-windows.md
Created July 4, 2026 14:18
Make symbolic links traverse over SSH on Windows 11 — disable RedirectionGuard for sshd (fixes STATUS_UNTRUSTED_MOUNT_POINT), with pre/post verification, a no-security-rollback alternative, and the mechanism.
@tandpfun
tandpfun / SKILL.md
Created July 14, 2026 02:16
Extract Clothing Skill
name extract-clothing-cutouts
description Extract high-quality, deduplicated transparent ecommerce clothing cutouts from a folder of photographs where people wear one or more garments. Use when Codex must find outfit or model photos, identify unique clothing across images, create focused references, reconstruct complete garments with Imagegen, remove a solid chroma background into RGBA PNGs, and output only the finished clothing images into a new folder under the current working directory.

Extract Clothing Cutouts

Turn photographs of worn clothing into source-faithful standalone catalog PNGs. Treat each result as a reconstruction from visible evidence, not literal segmentation whenever the wearer or another layer occludes part of the garment.

Start by asking for two paths

@Artefact2
Artefact2 / README.md
Last active July 14, 2026 11:58
GGUF quantizations overview

Which GGUF is right for me? (Opinionated)

Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggml-org/llama.cpp#5962

In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.

llama.cpp feature matrix

See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix

@dytroc
dytroc / widgetcreator.js
Last active July 14, 2026 11:56
// Base code: https://gist.github.com/aamiaa/7cdd590e3949cd654758bc90bcb4710b
let wpRequire = webpackChunkdiscord_developers.push([[Symbol()], {}, r => r]);
webpackChunkdiscord_developers.pop();
let ApexStore = Object.values(wpRequire.c).find(x => x?.exports?.A?.createOverride).exports.A;
let UserStore = Object.values(wpRequire.c).find(x => x?.exports?.A?.__proto__?.getCurrentUser).exports.A;
let FluxDispatcher = Object.values(wpRequire.c).find(x => x?.exports?.A?.__proto__?.flushWaitQueue).exports.A;
let api = Object.values(wpRequire.c).find(x => x?.exports?.Bo?.get).exports.Bo;
let globalCopy = navigator.userAgent.includes("Firefox") ? navigator.clipboard.writeText.bind(navigator.clipboard) : copy
@cereblab
cereblab / grok-build-cli-wire-analysis.md
Last active July 14, 2026 11:55
What xAI Grok Build CLI actually sends to xAI - a wire-level analysis (grok 0.2.93)

Update — 2026-07-14: This is the original wire-level analysis (grok 0.2.93). Since publishing: xAI disabled the upload server-side (disable_codebase_upload: true); added a /privacy opt-out — which I wire-tested and found to be a data-retention setting, not a block on what's sent; and Elon Musk publicly committed to deleting all previously-uploaded data (not yet confirmed complete). Full, maintained story + evidence: https://cereblab.com · https://github.com/cereblab/grok-build-exfil-repro

What xAI's Grok Build CLI Actually Sends to xAI: A Wire-Level Analysis

By @cereblab — Independent AI Safety Checker. Reproduce it yourself: github.com/cereblab/grok-build-exfil-repro

*A measured, reproducible teardown. Findings are backed by captured artifacts (endpoint, HTTP method, status code, byte size, host) and repro commands; where an observation was seen live but not retained as a file, §7 says so expli